Having been working with in the area of data and text analysis, and experiment-driven software development for a couple of years now, combined with my occasional enjoyment of well-made TV shows, I naturally found myself listening to Sebastian Wernicke’s TED talk to the finish. The talk is about using data to make (smart) decisions. I found the talk quiet relevant in today’s world of big data, and increased availability and accessibility to software usage data. Big data has rapidly moved into many real-life decision making processes in the workplace, law enforcement, medicine, etc., where serious decisions are being driven or aided by data.
What I liked from Wernicke’s TED talk titled ‘How to use data to make a hit TV Show’ was that it was a reminder that even though access to huge data has opened up many opportunities in various fields, data is still just a tool and decisions should not be solely driven by it. Wernicke emphasizes that the thing between our ears, i.e., brain (taking into account that that thing has the expertise to make sense of what the data is saying), should be the driver of decision-making.
In the talk, Wernicke gives two examples from two very competitive and data-savvy companies (Amazon and Netflix), who both collect and analyze millions of data points from their customers/users, to illustrate his point. In one example, big data was used successfully (in the case of Netflix with the House of Cards TV show) and in another example, not so successfully (case of Amazon in their creation of the Alpha House TV show).
As Wernicke explains, when Amazon wanted to create a TV show, they started by taking various ideas from people. From those ideas, they selected eight TV show candidates. Then they made a first episode of each one of these eight shows and put them online for free for the public to watch. Then Roy Price (Head of Amazon Studios) and the team at Amazon recorded everything, i.e., from when somebody pressed play, pressed pause, what parts they skipped, what parts they repeated, etc. They collected millions of data points because they want to use those data points to then decide which shows they should make. They did all the data crunching from those millions of collected data points and an answer emerged. And the answer in this case was that “Amazon should do a sitcom about four Republican US Senators”, and they made that show. They used the data to drive their decision making and ended up with a show that was not so successful – ‘Alpha House’ (Alpha House has an IMDB score of: 7.6).
This not so successful case of Alpha House was compared by Wernicke to the more successful story of Netflix in their creation of House of Cards (House of Cards has an IMDB score of: 9.0). Netflix approach was to start by looking at all the data they already had bout their viewers such as the viewer ratings, viewing history, and so on. Then they used that data to explore and discover little bits and pieces about the audience: what kinds of shows the viewers liked, the producers they liked, the actors, etc. With all these pieces collected, they took a risk and decided to license a drama series bout a single senator which was ‘House of Cards’.
Wernicke uses these two examples to explain the difference in how data can be used to make decisions. In the talk, he continues to explain that whenever we, as humans, are solving complex problems, we are essentially doing two things: The first is to break that problem into bits and pieces so that we can deeply analyze each of those bits and pieces. The second part involves then putting all these bits and pieces back together to come to our solution – and sometimes, this is an iterative process. Data and data analysis are only good for the first part, that is, no matter how powerful that data and data analysis is, it can only help us in taking a problem apart and understanding its pieces. It’s not suited to putting those pieces back together again and then to come to a conclusion. Wernicke points out that there is another tool that can put the pieces back together, and its available to all of all of us, this tool of course is the brain, and one of the things it is good at, is taking those bits and pieces back together again, even when there is incomplete information, and coming to a good conclusion. But to come to a good conclusion, that brain has to have some expertise.
Of course data helps us see what we might miss, find new avenues or business ideas. Thus in our data analysis work, we must get the balance right. Yes, “more data is better and can deliver brilliant insights, but in the end it has to be integrated by expert human brains for complex issues like producing a brilliant TV show”, Wernicke states. Humans as decision makers still need to be part of the data analysis equation, but what this also means for data scientists is also to have the expertise to make ‘often’ right conclusions or risks, Wernicke adds. The sentiment of the brain as the decision maker was also echoed by Beverly Wright, executive director at the business analytics center at Georgia Tech, in a Keynote Panel Discussion at Global Big Data Conference [Source].
From the two examples that Wernicke gives, making the right conclusions often as a data scientist or knowing when to take risks, comes with practice. For instance, from the time when those two TV show examples were created, both companies have learned a lot since then. For example, this year, 2016, Amazon had two original series nominated for the golden globe awards.